Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model. Issue 10 (28th September 2021)

• Record Type: Journal Article
• Authors: Lane, Rosanna A.; Freer, Jim E.; Coxon, Gemma; Wagener, Thorsten
• Is Part Of: Water resources research. Volume 57:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-28
• Subjects: regionalization -- parameterization -- hydrological modeling -- uncertainty -- Great Britain -- DECIPHeR; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR028393
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Spatial parameter fields are required to model hydrological processes across diverse landscapes. Transfer functions are often used to relate parameters to spatial catchment attributes, introducing large uncertainties. Quantifying these uncertainties remains a key challenge for large‐scale modeling. This paper extends the multiscale parameter regionalization (MPR) technique to consider parameter uncertainties. We evaluate this method of producing nationally consistent parameter fields, which maintain a constant relationship between model parameters and catchment attributes, across 437 catchments in Great Britain (GB). By sampling multiple transfer function parameters, we produce thousands of possible model parameter fields which are constrained within an uncertainty framework. This is compared to spatially homogeneous parameter sets constrained for individual catchments. The nationally consistent MPR parameter fields perform well (KGE* > 0.75) across 60% of catchments. Performance is similar or better than catchment‐constrained parameters (KGE* drop < 0.1) across 82% of catchments. Advantages of our national parameter fields include (a) improved representation of flows within catchments, (b) more robust performance between calibration and evaluation periods, and (c) spatial parameter fields reflecting hydrologically meaningful variation in catchment characteristics. By including uncertainties, we show that hydrographs produced using MPR have smaller uncertainty bounds which are better able to encompass flows. As the first application of MPR to both the DECIPHeR modeling framework and GB, we developed transfer functions and identified key catchment attributes to constrain model parameters, which are transferrable to other models alongside the addition of uncertainty. Methodologies presented here are informative for future regionalization efforts in GB and elsewhere. Key Points: By including uncertainties in multiscale parameter regionalization (MPR), we generate and evaluate ensembles of national model parameter fields Regionalized parameter fields result in more consistent performance and hydrographs with smaller uncertainty bounds than Monte‐Carlo constrained parameters The method of incorporating uncertainties, transfer functions, and selection of catchment attributes can be applied in future regionalization studies